Publications

Summary

In this paper, we propose an artificial market to model high-frequency trading where fast traders use threshold rules strategically to issue orders based on a signal reflecting the level of stochastic liquidity prevailing on the market. A market maker is in charge of adjusting prices (on a fast scale) and of setting closing prices and transaction costs on a daily basis, controlling for the volatility of returns and market activity. We first show that a baseline version of the model with no frictions is able to generate returns endowed with several stylized facts. This achievement suggests that the two time scales used in the model are one (possibly novel) way to obtain realistic market outcomes and that high-frequency trading can amplify liquidity shocks. We then explore whether transaction costs can be used to control excess volatility and improve market quality. While properly implemented taxation schemes may help in reducing volatility, care is needed to avoid excessively curbing activity in the market and intensifying the occurrence of abnormal peaks in returns.

Summary

Policy implications are derived for an inflation-targeting central bank, whose credibility is endogenous and depends on its past ability to achieve its targets. This is done in a New Keynesian framework with heterogeneous and boundedly rational expectations. We find that the region of allowed policy parameters is strictly larger than under rational expectations. However, when the zero lower bound on the nominal interest rate is accounted for, self-fulfilling deflationary spirals can occur, depending on the credibility of the central bank. Deflationary spirals can be prevented with a high inflation target and aggressive monetary easing.

Summary

We propose a nonparametric estimation for a class of fractional stochastic differential equations (FSDE) with random effects. We precisely consider general linear fractional stochastic differential equations with drift depending on random effects and non-random diffusion. We build ordinary kernel estimators and histogram estimators and study their risk (), when . Asymptotic results are evaluated as both and N tend to infinity.

Summary

Using a dynamic heterogeneous agent industry model, we examine the impact of manager remuneration schemes on firms’ investment decisions and on the evolution of their competitiveness and share values. Whereas an increase in the share-based manager remuneration component is always beneficial to the manager, it is beneficial for shareholders only if such a change in the remuneration scheme is adopted by all firms in the industry. In that case, productivity growth is slowed down and workers’ real wages are reduced.

Summary

Tracking the chain of events generated by an aggregate shock in an Agent Based Model (ABM) is apparently an impossible mission. Employing the methodology described in Assenza and Delli Gatti (J Econ Dyn Control 37(8):1659–1682 2013) (AD2013 hereafter), in the present paper we show that such a task can be carried out in a straightforward way by using a hybrid macro ABM consisting of a IS curve, an Aggregate Supply (AS) curve and a Taylor Rule (TR) in that aggregate investment is a function of the moments of the distribution of firms’ net worth. For each shock (fiscal expansion, monetary tightening, financial shock) we can decompose the change of the aggregate scale of activity (measured by the employment rate) in a first round effect – i.e., the change generated by the shock keeping the moments of the distribution of net worth at the pre-shock level – and a second round effect, i.e., the change brought about by the variation in the moments induced by the aggregate shock. In turn, the second round effect can be decomposed in a term that would show up also in a pure Representative Agent setting (RA component) and a term that is specific to the model with Heterogeneous Agents (HA component). In all the cases considered, the first round effect explains most of the actual change of the output gap. The second round effect is unambiguously negative. The HA component has the same sign of the RA component and explains a sizable fraction of the second round effect.

Summary

A very timely issue for economic agent-based models (ABMs) is their empirical estimation. This paper describes a line of research that could resolve the issue by using machine learning techniques, using multi-layer artificial neural networks (ANNs), or so called Deep Nets. The seminal contribution by Hinton et al. (Neural Comput 18(7):1527–1554, 2006) introduced a fast and efficient training algorithm called Deep Learning, and there have been major breakthroughs in machine learning ever since. Economics has not yet benefited from these developments, and therefore we believe that now is the right time to apply multi-layered ANNs and Deep Learning to ABMs in economics.

Summary

Diving behaviour of narwhals is still largely unknown. We use Hidden Markov models (HMMs) to describe the diving behaviour of a narwhal and fit the models to a three-dimensional response vector of maximum dive depth, duration of dives and post-dive surface time of 8,609 dives measured in East Greenland over 83 days, an extraordinarily long and rich data set. Narwhal diving patterns have not been analysed like this before, but in studies of other whale species, response variables have been assumed independent. We extend the existing models to allow for dependence between state distributions, and show that the dependence has an impact on the conclusions drawn about the diving behaviour. We try several HMMs with 2, 3 or 4 states, and with independent and dependent log-normal and gamma distributions, respectively, and different covariates to characterize dive patterns. In particular, diurnal patterns in diving behaviour is inferred, by using periodic B-splines with boundary knots in 0 and 24 hours.

Summary

The statistical problem of parameter estimation in partially observed hypoelliptic diffusion processes is naturally occurring in many applications. However, due to the noise structure, where the noise components of the different coordinates of the multi-dimensional process operate on different time scales, standard inference tools are ill conditioned. In this paper, we propose to use a higher order scheme to discretize the process and approximate the likelihood, such that the different time scales are appropriately accounted for. We show consistency and asymptotic normality with non-typical convergence rates. When only partial observations are available, we embed the approximation into a filtering algorithm for the unobserved coordinates, and use this as a building block in a Stochastic Approximation Expectation Maximization algorithm. We illustrate on simulated data from three models; the Harmonic Oscillator, the FitzHugh-Nagumo model used to model the membrane potential evolution in neuroscience, and the Synaptic Inhibition and Excitation model used for determination of neuronal synaptic input.

Summary

We investigate the interplay between technological change and macro- economic dynamics in an agent-based model of the formation of production networks. On the one hand, production networks form the structure that determines economic dynamics in the short run. On the other hand, their evolution reflects the long-term impacts of competition and innovation on the economy. We account for process innovation via increasing variety in the input mix and hence increasing connectivity in the network. In turn, product innovation induces a direct growth of the firm’s productivity and the potential destruction of links. The interplay between both processes generates complex technological dynamics in which phases of process and product innovation successively dominate. The model reproduces a wealth of stylized facts about industrial dynamics and technological progress, in particular the persistence of heterogeneity among firms and Wright’s law for the growth of productivity within a technological paradigm. We illustrate the potential of the model for the analysis of industrial policy via a preliminary set of policy experiments in which we investigate the impact on innovators’ success of feed-in tariffs and of priority market access.

Summary

We study a stochastic model of anonymous influence with conformist and anti-conformist individuals. Each agent with a ‘yes’ or ‘no’ initial opinion on a certain issue can change his opinion due to social influence. We consider anonymous influence, which depends on the number of agents having a certain opinion, but not on their identity. An individual is conformist/anti-conformist if his probability of saying ‘yes’ increases/decreases with the number of ‘yes’-agents. We focus on three classes of aggregation rules (pure conformism, pure anti-conformism, and mixed aggregation rules) and examine two types of society (without, and with mixed agents). For both types we provide a complete qualitative analysis of convergence, i.e., identify all absorbing classes and conditions for their occurrence. Also the pure case with infinitely many individuals is studied. We show that, as expected, the presence of anti-conformists in a society brings polarization and instability: polarization in two groups, fuzzy polarization (i.e., with blurred frontiers), cycles, periodic classes, as well as more or less chaotic situations where at any time step the set of ‘yes’-agents can be any subset of the society. Surprisingly, the presence of anti-conformists may also lead to opinion reversal: a majority group of conformists with a stable opinion can evolve by a cascade phenomenon towards the opposite opinion, and remains in this state.